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Advanced Sensing Technologies in Geotechnical Engineering—2nd Edition
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Advanced Sensing Technologies in Geotechnical Engineering—2nd Edition

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: 25 August 2025 | Viewed by 5554

Special Issue Editors

Dr. Mario Bačić

E-Mail Website
Guest Editor
Faculty of Civil Engineering, University of Zagreb, 10000 Zagreb, Croatia
Interests: nondestructive testing methods; geotechnical monitoring; numerical modeling in geotechnics; risk assessment; climate change adaptation; energy geostructures
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Meho-Saša Kovačević

E-Mail Website
Guest Editor
Faculty of Civil Engineering, University of Zagreb, 10000 Zagreb, Croatia
Interests: soil and rock investigation works; nondestructive testing methods; geophysical methods; geotechnical monitoring; remote sensing; soil and rock mechanics; shallow geothermal energy
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Kenneth Gavin

E-Mail Website
Guest Editor
Faculty of Civil Engineering and Geosciences, TU Delft, 2628 CN Delft, The Netherlands
Interests: offshore foundations; the effect of climate change on critical infrastructure; soil-structure interaction; risk in geotechnical engineering
Dr. Vassilis Marinos

E-Mail Website
Guest Editor
Geotechnical Department, School of Civil Engineering, National Technical University of Athens (NTUA), 157 80 Athens, Greece‎
Interests: tunnelling; rockfall analysis; geohazards correlated to natural gas pipelines; dam foundation; rock mass structural analysis; landslide hazard and stability analysis and monitoring
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Tadeusz Stepinski

E-Mail Website
Guest Editor
1. Department of Robotics and Mechatronics, AGH University of Science and Technology, 30-059 Kraków, Poland
2. Professor Emeritus, Department of Engineering Sciences, Uppsala University, 752 36 Uppsala, Sweden
Interests: ultrasound techniques; nondestructive evaluation; structural health monitoring; electromagnetic techniques
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The first edition of the ‘Advanced Sensing Technologies in Geotechnical Engineering’ Special Issue gained large success, with many papers published, dealing with various aspects of geotechnical monitoring. Owing to the success of the first volume, we have edited this second one, which aims to collect and present latest innovative research on in situ and remote sensing technologies in the fields of geotechnical engineering, geology, hydrogeology, environmental engineering, and geodesy, which enhance the knowledge and understanding of soil and rock behaviour during and after construction works or geo-hazards. In addition, omnipresent climate changes significantly influence the behaviour of soil, rock, and geotechnical structures, where the prediction of their response should be based on the monitoring data.

However, predicting the behaviour of soil, rock, and geotechnical structures is very complex and burdened with numerous uncertainties. To enhance the insight into such behaviour, whether through the conduction of investigation work, verification of design solutions, or quality control works, numerous in situ and remote sensing methods are available for scientists and practitioners. Useful information can be obtained through the installation of the monitoring equipment, where changes in measurement results (displacement, deformation, strain, stress, pore pressures, etc.) may point to a limit state exceedance mechanism. Significant development in this field is evident in the last few decades, with the monitoring methods taking advantage of modern sensor types such as piezoelectric sensors, optic fibres, etc. Additionally, the development of innovative geodetic sensing techniques has significantly boosted monitoring activities, where highly accurate remote measurements can be used to determine the extent of deformation / displacement of soil and rock in greater areas. Modern signal processing tools, as well as other advanced computing systems such as neural networks, further open doors to the continuous development of sensor-based instrumentation.

Papers focusing on theoretical and experimental aspects of terrestrial and aerial sensing, including instrumentation, data acquisition, data analysis, processing, and interpretation are highly encouraged, especially those involving field case study implementations and validations. The overall aim of this Special Issue of Sensors is to provide new insights, advances, and approaches in the application of original and innovative sensing technologies in field of geotechnical engineering and other related geo-sciences.

Dr. Mario Bačić
Prof. Dr. Meho-Saša Kovačević
Prof. Dr. Kenneth Gavin
Dr. Vassilis Marinos
Prof. Dr. Tadeusz Stepinski
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • geotechnical monitoring
  • in situ monitoring of soil and rock
  • remote sensing of soil and rock
  • sensor-based instrumentation
  • monitoring data analysis
  • signal processing

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Published Papers (4 papers)

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Research

23 pages, 8475 KiB  
Article
Analyzing the Effect of Drainage on the Stability of Tailings Dams Using the Interpretation of Cross-Correlations
by Moustafa Hamze-Guilart, Lineu Azuaga Ayres da Silva, Anna Luiza Marques Ayres da Silva and Maria Eugenia Gimenez Boscov
Sensors 2025, 25(6), 1833; https://doi.org/10.3390/s25061833 - 15 Mar 2025
Viewed by 361
Abstract
Over the years, multiple tailings dam failures all over the world have been primarily linked to drainage issues. Given its critical role in dam stability, this research analyzes the relationship between precipitation, reservoir levels, and geotechnical instrumentation measurements along the elevation stages of [...] Read more.
Over the years, multiple tailings dam failures all over the world have been primarily linked to drainage issues. Given its critical role in dam stability, this research analyzes the relationship between precipitation, reservoir levels, and geotechnical instrumentation measurements along the elevation stages of a tailings dam. To assess the influence of drainage on dam performance, its dependence on infiltration, reservoir water fluctuations, and geotechnical instrumentation responses was modeled and interpreted. By applying time series analysis methods to the instrumentation data, including autocorrelation and cross-correlation functions, this study identifies patterns in drainage efficiency and its impact on stability. The time series data were regularized and transformed into stationary forms to ensure consistency in the analysis. Autocorrelation functions and cross-correlations between different monitoring instruments were computed specifically for the second to the seventh elevation stages of the tailings dam. This study focuses on four cross-sections of the dam, analyzing their behavior to differentiate the spatial and temporal effects of drainage. The results reveal variations in drainage efficiency across these different sections and elevation stages, providing a deeper understanding of the role of drainage in maintaining stability. The proposed methodology can also be successfully applied to other tailings storage facilities, such as tailings dams built downstream or dry stacking piles, contributing to improved monitoring and risk assessment strategies. Full article
(This article belongs to the Special Issue Advanced Sensing Technologies in Geotechnical Engineering—2nd Edition)
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19 pages, 1351 KiB  
Article
An Open-Source Algorithm for Correcting Stress Wave Dispersion in Split-Hopkinson Pressure Bar Experiments
by Arthur Van Lerberghe, Kin Shing O. Li, Andrew D. Barr and Sam D. Clarke
Sensors 2025, 25(1), 281; https://doi.org/10.3390/s25010281 - 6 Jan 2025
Cited by 2 | Viewed by 709
Abstract
Stress wave dispersion can result in the loss or distortion of critical high-frequency data during high-strain-rate material tests or blast loading experiments. The purpose of this work is to demonstrate the benefits of correcting stress wave dispersion in split-Hopkinson pressure bar experiments under [...] Read more.
Stress wave dispersion can result in the loss or distortion of critical high-frequency data during high-strain-rate material tests or blast loading experiments. The purpose of this work is to demonstrate the benefits of correcting stress wave dispersion in split-Hopkinson pressure bar experiments under various testing situations. To do this, an innovative computational algorithm, SHPB_Processing.py, is created. Following the operational run through of SHPB_Processing.py’s capabilities, it is used to process test data acquired from split-Hopkinson pressure bar tests on aluminium, sand and kaolin clay samples, under various testing conditions. When comparing dispersion corrected and simple time shifting data obtained from SHPB experiments, accounting for dispersion removes spurious oscillations and improves the inferred measurement at the front of the specimen. The precision of the stress and strain results gathered from its application emphasises its importance through the striking contrast between its application and omission. This has a significant impact on the validity, accuracy and quality of the results. As a result, in the future, this tool can be utilised for any strain rate testing situation with cylindrical bars that necessitates dispersion correction, confinement, or stress equilibrium analysis. Full article
(This article belongs to the Special Issue Advanced Sensing Technologies in Geotechnical Engineering—2nd Edition)
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28 pages, 8288 KiB  
Article
Geotechnical Assessment of Foundation Stability for Preserving the Agrippa Monument at the Acropolis of Athens
by Vassilis Marinos, Georgios Prountzopoulos, Dimitra Papouli, Dionisia Michalopoulou and Vassiliki Eleftheriou
Sensors 2025, 25(1), 219; https://doi.org/10.3390/s25010219 - 2 Jan 2025
Viewed by 757
Abstract
This study focuses on the geotechnical evaluation of the foundation conditions of the Agrippa Monument at the Acropolis of Athens, aiming to propose interventions to improve stability and reduce associated risks. The assessment reveals highly uneven foundation conditions beneath the monument. A thorough [...] Read more.
This study focuses on the geotechnical evaluation of the foundation conditions of the Agrippa Monument at the Acropolis of Athens, aiming to propose interventions to improve stability and reduce associated risks. The assessment reveals highly uneven foundation conditions beneath the monument. A thorough collection of bibliographic references and geotechnical surveys was conducted, classifying geomaterials into engineering-geological units and evaluating critical parameters for geotechnical design. Geotechnical models were developed and 3D finite element analyses were performed. The qualitative evaluation of the foundation under static conditions indicates no immediate risk of failure, as no accelerated movement has been observed and the monument’s tilt remains well below critical values. Time-dependent settlements are not expected from any clay layers in the artificial fills. However, further soil compaction could occur due to seismic events, water action (causing erosion or voids), or changes in the monument’s weight or tilt under static conditions. The study also proposes instrumental monitoring, foundation soil improvement, and water management strategies to enhance the monument’s stability and mitigate potential risks. Full article
(This article belongs to the Special Issue Advanced Sensing Technologies in Geotechnical Engineering—2nd Edition)
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17 pages, 7474 KiB  
Article
Normalizing Large Scale Sensor-Based MWD Data: An Automated Method toward A Unified Database
by Abbas Abbaszadeh Shahri, Chunling Shan, Stefan Larsson and Fredrik Johansson
Sensors 2024, 24(4), 1209; https://doi.org/10.3390/s24041209 - 14 Feb 2024
Cited by 30 | Viewed by 2722
Abstract
In the context of geo-infrastructures and specifically tunneling projects, analyzing the large-scale sensor-based measurement-while-drilling (MWD) data plays a pivotal role in assessing rock engineering conditions. However, handling the big MWD data due to multiform stacking is a time-consuming and challenging task. Extracting valuable [...] Read more.
In the context of geo-infrastructures and specifically tunneling projects, analyzing the large-scale sensor-based measurement-while-drilling (MWD) data plays a pivotal role in assessing rock engineering conditions. However, handling the big MWD data due to multiform stacking is a time-consuming and challenging task. Extracting valuable insights and improving the accuracy of geoengineering interpretations from MWD data necessitates a combination of domain expertise and data science skills in an iterative process. To address these challenges and efficiently normalize and filter out noisy data, an automated processing approach integrating the stepwise technique, mode, and percentile gate bands for both single and peer group-based holes was developed. Subsequently, the mathematical concept of a novel normalizing index for classifying such big datasets was also presented. The visualized results from different geo-infrastructure datasets in Sweden indicated that outliers and noisy data can more efficiently be eliminated using single hole-based normalizing. Additionally, a relational unified PostgreSQL database was created to store and automatically transfer the processed and raw MWD as well as real time grouting data that offers a cost effective and efficient data extraction tool. The generated database is expected to facilitate in-depth investigations and enable application of the artificial intelligence (AI) techniques to predict rock quality conditions and design appropriate support systems based on MWD data. Full article
(This article belongs to the Special Issue Advanced Sensing Technologies in Geotechnical Engineering—2nd Edition)
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